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Advanced Regression Modeling for Autonomous Wall-Following System

Explore ARMAX and RBF models for laser sensor-based wall-following, with correlations and training insights. Novelty detection and imitation learning techniques also discussed.

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Advanced Regression Modeling for Autonomous Wall-Following System

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Presentation Transcript

  1. System Identification (Ulrich Nehmzow) • Random data • Built ARMAX model  worked well for different regression orders • Trained RBF on data  worked for ideal outputs, not for entire data • Wallfollowing • Computed correlations between 3 laser sensors and output (rot_speed): 45° 90° 135° • Built ARMAX model to predict output from laser input; resulting coefficients corresponded to sensor correlation; Spearman rank  .89 • Trained RBF; Spearman rank  .50 to .91, depending on parameters

  2. Imitation learning (Jan Peters) • Approaching red static object by steering Eddy

  3. Imitation learning (Jan Peters) (2) Training to follow moving object by steering Eddy

  4. Imitation learning (Jan Peters) Result: Action-state space

  5. Imitation learning (Jan Peters) (3) Eddy, imitating object-following behaviour autonomously using learned regression model

  6. Novelty Detection (Ulrich Nehmzow) • Build normality matrix and find specified outliers (2) Find one outlier in sensor data

  7. RNNPB learning (Jun Tani)

  8. We had fun learning!

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